What artificial intelligence can teach us about proteins

Intelligent virtual companions like Alexa, Siri, and Google
Assistant have long become integrated into our everyday lives.
And intelligent computational programs, so-called algorithms,
have also evolved as an integral tool in scientific research. The
huge amounts of data generated in life science research can be
efficiently examined for recurring patterns with the aid of
algorithms. Certain programs are able to spot recurring
structures in large protein molecules and then use this
information to draw conclusions about what cellular tasks these
molecules perform -- for example, whether they function as gene
switches, molecular motors, or signaling molecules. The
predictions made by such algorithms on the basis of protein
sequences -- which consist of a series of protein building blocks
strung together like a pearl necklace -- are now incredibly
precise.

However, a major disadvantage of previous techniques is that
users are kept completely in the dark as to why the algorithm
assigns a particular function to certain protein sequences. The
computer's precise knowledge about proteins is not directly
available, despite the fact that such knowledge could prove
invaluable in advancing the research and development of new
agents.

A student team, jointly led by Roland Eils and Irina Lehmann
from the Berlin Institute of Health (BIH) and Charité --
Universitätsmedizin Berlin, in collaboration with Dominik
Niopek from the Institute of Pharmacy and Molecular
Biotechnology (IPMB) at Heidelberg University, set itself the
goal of unlocking this knowledge from the computer. It began
working on this topic in 2017, and has developed an algorithm
called "DeeProtein," a comprehensive and intelligent neural
network that can predict the functions of proteins based on the
sequence of individual protein building blocks, the amino
acids. Like most learning algorithms, DeeProtein is a "black
box," which means how they work remains a mystery to the
programmers as well as the users. But the students have now
used a "trick" to unravel the secret of this network.

The young scientists started by developing a way to
figuratively look over the shoulder of the program as it does
its work. "In the sensitivity analysis we successively mask
each position in the protein sequence and let DeeProtein
calculate, or rather predict, the function of the protein from
this incomplete information," explains Julius Upmeier zu
Belzen. He is a student in the master's program in molecular
biotechnology at the IPMB and the lead author of the paper,
which was just published in the journal Nature Machine
Intelligence*. "Next we give DeeProtein the complete
sequence information and compare the two sets of predictions,"
adds Upmeier zu Belzen. "In this way we calculate, for each
position in the protein sequence, how important this position
is for predicting the correct function. This means that we give
each position or amino acid in the protein chain a sensitivity
value for the protein function."

The scientists then use the new analytical technique to
identify the regions of the proteins that are vital to their
function. This technique works for signaling proteins that play
a role during carcinogenesis as well for the CRISPR-Cas9
gene-editing tool, which has already been tested in a large
number of preclinical and clinical studies. "The sensitivity
analysis enables us to identify protein regions that tolerate
changes well or not so well," says Dominik Niopek. "This is an
important first step if we want to make targeted changes to
proteins, so as to equip them with new functions or to 'switch
off' undesirable properties."

"With this work we show that not only can the predictions of
neural networks be helpful, but that we can also now for the
first time use this implicit knowledge for practical ends,"
explains Roland Eils. This approach is relevant for many issues
in molecular biology and medicine. "If, for example, we want to
develop targeted drugs or gene therapies, we need to know
exactly where to focus our attention," adds Eils. "DeeProtein
can now help us do that."

"The defendant took advantage of them emotionally and sexually," Assistant U.S....

News Fuzzer is a centralized news magazine, we are collecting the latest world news from the most popular sources and classifying it on multiple categories: International news, UK news, US news, Sport news, Cybersecurity News, Economic News, Politics, Health, Science, Cryptocurrency news and many more.